High-Fidelity 3D Gaussian Splatting for Exposure-Bracketing Space Target Reconstruction: OBB-Guided Regional Densification with Sobel Edge Regularization

In this paper, a novel optimization framework based on 3D Gaussian splatting (3DGS) for high-fidelity 3D reconstruction of space targets under exposure bracketing conditions is studied. In the considered scenario, multi-view optical imagery captures space targets under complex and dynamic illumination, where severe inter-frame brightness variations degrade reconstruction quality by introducing photometric inconsistencies and blurring fine geometric details. Unlike existing methods, we explicitly address these challenges by integrating exposure-aware adaptive refinement and edge-preserving regularization into the 3DGS pipeline. Specifically, we propose an exposure bracketing-oriented bounding box (OBB) regional densification strategy to dynamically identify and refine under-reconstructed regions. In addition, we introduce a Sobel edge regularization mechanism to guide the learning of sharp geometric features and improve texture fidelity. To validate the framework, experiments are conducted on both a custom OBR-ST dataset and the public SHIRT dataset, demonstrating that our method significantly outperforms state-of-the-art techniques in geometric accuracy and visual quality under exposure-bracketing scenarios. The results highlight the effectiveness of our approach in enabling robust in-orbit perception for space applications.